论文标题
CLAS12漂移室中轨道重建的自动编码器
Auto-encoders for Track Reconstruction in Drift Chambers for CLAS12
论文作者
论文摘要
在本文中，我们描述了机器学习模型的开发，以通过推断漂移室中缺少的段来识别轨道来帮助CLAS12跟踪算法。自动编码器用于重建轨道轨迹缺失的段。实现的神经网络能够以$ \ $ \ 0.35 $的电线的精度可靠地重建缺失的段位置，并导致丢失的轨道的恢复，准确性> 99.8 \％$ $。
In this article we describe the development of machine learning models to assist the CLAS12 tracking algorithm by identifying tracks through inferring missing segments in the drift chambers. Auto encoders are used to reconstruct missing segments from track trajectory. Implemented neural network was able to reliably reconstruct missing segment positions with accuracy of $\approx 0.35$ wires, and lead to recovery of missing tracks with accuracy of $>99.8\%$.
